PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 11; p. 3342
• Main Authors: Deng, Yi, Liu, Jiazheng, Zhu, Kuihu, Xie, Quan, Liu, Hai
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.05.2024; MDPI
• Subjects: Accuracy; Artificial intelligence; Computational linguistics; Deep learning; Electric fields; Electric transformers; extracted features; feature fusion; Identification; Language processing; multiscale; Natural language interfaces; Neural networks; Optimization; partial discharge; Pattern recognition; PRPD; Radiation; Sensors; Workloads; extracted features; PRPD; feature fusion; multiscale; partial discharge
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24113342

Partial discharge (PD) is a localized discharge phenomenon in the insulator of electrical equipment resulting from the electric field strength exceeding the local dielectric breakdown electric field. Partial-discharge signal identification is an important means of assessing the insulation status of electrical equipment and critical to the safe operation of electrical equipment. The identification effect of traditional methods is not ideal because the PD signal collected is subject to strong noise interference. To overcome noise interference, quickly and accurately identify PD signals, and eliminate potential safety hazards, this study proposes a PD signal identification method based on multiscale feature fusion. The method improves identification efficiency through the multiscale feature fusion and feature aggregation of phase-resolved partial-discharge (PRPD) diagrams by using PMSNet. The whole network consists of three parts: a CNN backbone composed of a multiscale feature fusion pyramid, a down-sampling feature enhancement (DSFB) module for each layer of the pyramid to acquire features from different layers, a Transformer encoder module dominated by a spatial interaction–attention mechanism to enhance subspace feature interactions, a final categorized feature recognition method for the PRPD maps and a final classification feature generation module (F-Collect). PMSNet improves recognition accuracy by 10% compared with traditional high-frequency current detection methods and current pulse detection methods. On the PRPD dataset, the validation accuracy of PMSNet is above 80%, the validation loss is about 0.3%, and the training accuracy exceeds 85%. Experimental results show that the use of PMSNet can greatly improve the recognition accuracy and robustness of PD signals and has good practicality and application prospects.